In our experiments
we employ a Deeplab-V2 [1]} model with a ResNet-101 [2]} backbone pretrained on ImageNet [3]}, as the base semantic segmentation (student) network \(f_{\theta }\) .
We use the Pytorch deep learning framework to implement our approach on two NVIDIA-SMI GPUs with 16 GB memory in total. The batch size for the training is set to 1 and 4 for the source and target domains respectively. For the target domain we use 4 samples in each batch where 2 are labeled images and 2 are unlabeled. We did not attempt to tune this batch size parameter.
During training, Stochastic Gradient Descent (SGD) is employed as the optimizer with momentum of \(0.9\) . We use a polynomial decay schedule [1]}.
The initial learning rate and weight decay are set to \(5 \times 10^{-4}\)  and \(2.5 \times 10^{-4}\)  respectively. The weight \(\lambda \)  in Eq. REF  is set to 1.
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